New Release Highlights!

New Release Highlights!

Image size is no longer set to a default value and it will automatically determine the image size based on the model instance.

Enhanced training demos to allow users to generate images from the trained models.

Updated SBML-spatial support in keeping with v0.89 of the specification.

October 5, 2014Seeking investigators for Collaborative or Service projects through the National Center for Multiscale
Modeling of Biological Systems

The image analysis and modeling team at the NIH-supported
National Center for Multiscale Modeling of Biological
Systems would like to bring
to your attention an opportunity to engage in a collaborative or service
project with researchers at the Center. We are seeking investigators who wish to use CellOrganizer for
learning and using generative models of cell size, shape and subcellular
organization (or to help with further development).
We can provide extensive training to
external personnel, consultation on appropriate methods and design of studies, help
with local installation of any desired software, and access to computational
resources at the Center for image analysis, modeling and simulation. CellOrganizer learns
modular models of things such as cell shape, nuclear shape, vesicular organelle
distribution and microtubule distribution directly from 2D or 3D images and can
produce specific instances of cell geometries without the need to create them
by hand or to segment microscope images (see Buck et al, 2012
for an overview). Through Center
funding, pipelines have been created whereby these geometries can be combined
with biochemical models to perform spatially realistic cell simulations with a
minimum of effort (Center resources can be provided to run these using the cell
simulation engine MCell. The biochemical models can be encoded in
SBML (i.e., investigator created or downloaded from models databases) or can be
generated by BioNetGen (a powerful rule-based
modeling package). This combination
of CellOrganizer and MCell
allows investigators to explore the effect of different cell geometries on
their models (e.g., to independently explore different modes of variation in the
generative models, such as variation in organelle number vs. shape). Existing generative models of 3T3 cells,
HeLa cells, and C2C12 cells can be used so that making
extensive image collections can be avoided.

If interested, please contact murphy@cmu.edu
or fill out the form at the MMBioS web site. We would be happy to further explain the
capabilities of the current system and discuss development of new capabilities.

The CellOrganizer project provides tools for

learning generative models of cell organization directly from images

storing and retrieving those models in XML files

synthesizing cell images (or other representations) from one or more models

Model learning captures variation among cells in a collection of images. Images used for model learning and
instances synthesized from models can be two- or three-dimensional static images or movies.

CellOrganizer can
learn models of

cell shape

nuclear shape

chromatin texture

vesicular organelle size, shape and position

microtubule distribution.

These models can be cossnditional upon each other. For example, for a given synthesized cell instance, organelle position is dependent upon the cell and nuclear shape of that instance.

Cell types for which generative models for at least some organelles have been built include human HeLa cells, mouse NIH 3T3 cells, and Arabidopsis protoplasts. Planned projects include mouse T lymphocytes and rat PC12 cells.